VOC Growth Report

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name: voc-growth-report description: Turn exported social media comments — especially Xiaohongshu/小红书 CSV exports from 社媒助手 — into VOC insight, growth analysis, Feishu-ready operating structures, and boss-ready HTML report delivery links. Use this whenever the user wants to analyze comment CSVs, extract user sentiment/needs/commercial intent, segment audiences, build VOC reports, generate HTML decision reports, create Feishu/Bitable comment libraries, or turn comment exports into growth recommendations. Make sure to use this skill when the user mentions 社媒助手, 评论抓取, 评论CSV, VOC分析, 用户需求洞察, 商机分析, 飞书评论库, HTML报告, Trae/Cursor/Claude Code report prompts, or asks for a link instead of raw HTML code.

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VOC Growth Report

This skill converts exported social comment data into a repeatable growth-analysis workflow.

The core idea is simple: 1. ingest a CSV export, 2. analyze comments through a VOC + growth lens, 3. generate a boss-ready HTML report, 4. prefer delivering a preview link/path instead of dumping raw HTML.

Use this skill especially for 小红书 / 社媒助手 CSV exports, but it also works for similar social comment exports.

What this skill should produce

Depending on the user's ask, produce one or more of these:

a cleaned analysis brief,

a prompt pack for Trae / Cursor / Claude Code / Codex,

a field schema for Feishu Bitable,

a boss-ready HTML report prompt,

a local preview link delivery workflow.

Default workflow

Step 1: Confirm the real deliverable

First identify which of these the user actually wants:

analysis only: sentiment / needs / intent / opportunity

report prompt: a prompt for another coding agent to generate the report

report artifact: a real HTML file or preview link

Feishu workflow: import/sync results into Feishu / Bitable

skill/systemization: package the whole VOC workflow into a reusable system

If the user says things like:

“不要给我代码，给我链接”

“社媒助手抓完 csv 后怎么交给 Trae”

“给我老板能看的报告”

then optimize for delivery, not code verbosity.

Step 2: Understand the input data

Identify or ask for:

CSV path or file

likely columns: comment text, username, time, likes, replies, post title, link, platform

source platform / export tool

time range / sample size if relevant

If columns differ, infer the closest mapping instead of blocking on exact names.

This skill has already been validated against a real 社媒助手 / 小红书 comment export structure with fields like:

评论ID

评论内容

点赞量

评论时间

IP地址

子评论数

笔记ID / 笔记链接

用户ID / 用户链接 / 用户名称

一级评论ID / 一级评论内容

引用的评论ID / 引用的评论内容 / 引用的用户名称

Step 3: Analyze comments in 4 layers

When doing actual VOC analysis, prefer this four-layer model:

#### 1. Emotion Classify into:

正向

中性

负向

Output:

distribution

positive highlights

negative complaints

#### 2. Intent Classify into:

咨询价格

咨询功能

咨询购买

使用反馈

吐槽抱怨

夸赞认可

对比竞品

无效灌水

其他

Output:

type distribution

representative comments

common questions

#### 3. Commercial opportunity Classify into:

高

中

低

无

Use these definitions:

高：明确咨询价格、购买方式、联系方式、合作、试用、下单

中：明确咨询功能、效果、适用人群、区别、使用方法

低：普通兴趣表达、轻度认可、一般互动

无：灌水、无关内容、纯表情

Output:

opportunity distribution

top high-opportunity comments

conversion blockers

#### 4. Need discovery Split needs into:

已被满足的需求

未被满足的需求

潜在需求

Important: latent needs must be inferred from actual complaints, hesitation, comparisons, or repeated asks — never from pure imagination.

Output:

need categories

representative comments

why each need is classified that way

Step 4: Upgrade analysis into growth decisions

Do not stop at “analysis”. Convert outputs into growth decisions:

who to prioritize,

what pain points to solve first,

what value propositions to amplify,

what content topics to create,

what sales talking points to use,

what operations team should reply to first.

When appropriate, use a Kotler-flavored framing:

segmentation,

need discovery,

value proposition mapping,

conversion opportunity,

growth actions.

Default report structure

For boss/CEO-ready reports, prefer this structure:

1. 封面 / 数据概况 2. 用户情绪总览 3. 用户分群分析 4. 用户需求图谱 5. 商机与转化机会 6. 价值主张与增长建议 7. CEO Summary

Delivery-first rule

If the user wants a usable deliverable, do not stop at raw HTML code. Prefer to instruct the coding agent / ACP harness to: 1. generate the HTML, 2. save it to a file, 3. start a local static preview, 4. return a preview link and file path.

Use language like:

“你的任务不是输出源码，而是完成交付”

“最终返回访问链接、本地文件路径、报告标题、简短说明”

Output modes

Mode A: Prompt pack

When the user wants something to paste into Trae / Cursor / Claude Code / Codex, provide:

one consolidated instruction block,

explicit input/output contract,

delivery requirement: link > raw code.

Mode B: Feishu workflow

When the user wants Feishu integration, provide:

comment library field schema,

suggested analysis fields,

optional Bitable views,

minimal workflow from CSV/comment sync to reporting.

Recommended 12-field base schema:

平台

帖子标题

帖子链接

评论内容

评论用户

评论时间

情绪倾向

意图类型

商机等级

是否需要回复

跟进状态

备注

Mode C: Executive summary

For direct advice in chat, use this order: 1. conclusion, 2. why, 3. next action.

Keep it concise and business-oriented.

Example trigger cases

“帮我把社媒助手抓下来的评论 csv 做成老板能看的报告”

“不要给我 html 代码，我要最终链接”

“帮我做小红书 voc 分析”

“把评论做成需求洞察 + 商机分析”

“给 Trae 一段完整指令，从 csv 到 html 报告链接”

“封装一个 VOC 分析 skill”

Anti-patterns

Avoid these mistakes:

stopping at sentiment only,

giving a word cloud as the main output,

dumping raw HTML when the user asked for delivery,

inventing latent needs with no textual basis,

overcomplicating the workflow before the CSV/report path is usable.

Success standard

A strong result should make it easy for the user to go from: comment export → user insight → growth decisions → report delivery with minimal repeated prompting.

A stronger result should also be capable of producing a real executive-facing HTML demo report with sections such as:

封面 / 数据概况

用户情绪总览

用户分群分析

用户需求图谱

商机与转化机会

价值主张与增长建议

CEO Summary